Research Article
5 May 2026
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- Conference Proceedings2 April 2026
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- Research Article22 January 2026
IPO Pricing and Performance: Market Insights with a Green Lens
Background: Research on Initial Public Offerings (IPOs) in emerging markets has largely centred on under-pricing and short-term returns, with limited attention to sustainability-oriented issuers. This study addresses that gap by examining the role of green innovation, defined as IPOs issued by firms with explicit environmental sustainability frameworks in shaping IPO outcomes in India. Subject: The analysis covers 393 IPOs listed on the National Stock Exchange between 2012 and 2023, distinguishing Green and Non-Green issuers to assess comparative market performance under varying conditions and price bands. Materials and Methods: The Normalized Raw Return Spread (NRRS) methodology and multiple regression analysis are employed to evaluate market-adjusted returns across listing day and twelve-month horizons. IPOs are categorized by issue price and market capitalization, with particular focus on small-cap offerings priced below ₹150. Results: Green IPOs demonstrated moderate listing gains and higher short-term volatility but consistently outperformed non-green IPOs in long-term returns, especially beyond the sixth month. Small-cap green IPOs generated peak twelve-month returns of up to 147.74% in bullish market phases. However, regression analysis revealed that green status alone was not a statistically significant predictor of IPO performance after accounting for other factors; sustained post-listing momentum was the primary driver of returns. Conclusion: By integrating sustainability and innovation metrics into IPO analysis, the study challenges the assumption that returns peak on listing day. The findings have both theoretical and policy implications, underscoring India’s evolving shift toward sustainability-driven capital markets and positioning Green IPOs as catalysts for long-term economic and environmental value. - Research Article24 January 2026
From Operations to Outcomes: The Role of Industry 4.0 in Customer Management and Financial Performance
Despite extensive research on Industry 4.0 (I4.0) technologies, relatively little attention has been given to their impact on financial performance at the firm level. This study proposes a model to investigate the links between I4.0 implementation level (I4.0IL), supply chain management performance (SCMP), customer management performance (CMP), and company financial outcomes using bootstrap-based structural equation modeling (SEM) with data from 91 companies across various sectors. The results of the bootstrap-based SEM with 1,000 resamples show that SCMP significantly influences both CMP and company financial performance, and CMP also significantly impacts financial performance. While I4.0IL does not significantly amplify the effects of SCMP on CMP and financial performance, we find that I4.0IL primarily influences company financial performance through its moderating impact on the relationship between CMP and financial outcomes. Further analysis reveals that CMP significantly mediates the relationship between SCMP and financial outcomes. Our findings contribute to a deeper understanding of the financial effects associated with I4.0 technologies and shed light on the pathways through which I4.0ILs impact financial outcomes - Case Report24 January 2026
AJC Edutech: Scaling the Heights or Rethinking of a Sustainable Business Model?
This case study explores the strategic crossroads faced by AJC Edutech Private Limited (branded as anujjindal.in), a rapidly growing EdTech firm specializing in government exam preparation in India. Operating in a highly competitive and evolving digital education landscape, AJC Edutech confronts mounting pressure to sustain profitability, retain learner engagement, and differentiate its offerings amidst dominant players like BYJU’S, Unacademy, and Physics Wallah. The case investigates critical issues such as pricing innovation, product bundling, customer acquisition, and longterm brand positioning. It also examines the potential of technology integration, datadriven personalization, and community building as levers for growth. Through a detailed exploration of the company's operational model, market dynamics, and student expectations, the case poses a fundamental question: Should AJC Edutech scale aggressively to capture market share, or pause to rethink and consolidate its business model for long-term sustainability? Designed for courses in business strategy, digital entrepreneurship, and innovation management, the case encourages learners to evaluate real-world challenges of scaling, pivoting, and adapting in a volatile, tech-driven educational environment. It also includes simulation-based classroom activities and role-plays to deepen understanding of strategic decision making under resource constraints. This case offers valuable insights for educators, practitioners, and policy thinkers in the digital learning space. - Research Article30 March 2026
From Automation to Innovation: The Transformative Impact of AI on Business Functions in India
Background: Artificial Intelligence (AI) is revolutionizing business operations by driving innovation, streamlining processes, and enhancing decision-making. Its growing adoption across industries signifies a paradigm shift from traditional business models to intelligent, data-driven systems. Objective: The study aims to examine how AI influences modern business functions, with a particular focus on marketing practices within the Indian context. Methods: A qualitative research approach was employed using semi-structured interviews with ten Indian marketing professionals. The insights gathered were analyzed to identify emerging themes related to AI adoption, benefits, and challenges. Results: Findings reveal that AI significantly enhances operational efficiency by automating repetitive tasks, improving customer engagement through personalization, and providing data-driven insights for strategic decisions. Industries such as retail, e-commerce, technology, and finance have witnessed major transformations through AI applications like predictive analytics, fraud detection, and customer service automation. However, challenges such as poor data quality, integration with legacy systems, shortage of skilled talent, and ethical issues including data privacy and algorithmic bias persist. Conclusion: AI serves as a strategic enabler, transforming conventional business models into intelligent and adaptive systems. The study underscores the importance of ethical AI implementation, workforce upskilling, and strategic alignment of AI capabilities with organizational goals. These insights contribute to a deeper understanding of AI’s transformative role in Indian businesses and offer practical guidance for sustainable AI integration. - Conference Proceedings2 April 2026
- Research Article5 May 2026
Modern Retail Experience Design for Gen Z and Millennials in India: The Roles of Convenience, Personalization, and Sustainability
India’s retail sector is undergoing dramatic change, increasingly defined as an omnichannel journey in which shoppers navigate marketplaces, social media, quick-commerce apps, and physical stores. Focusing on Gen Z and Millennial consumers, this study tests whether three retailer-controlled experience levers, consumer convenience, personalized shopping experience, and sustainability and social responsibility (SSR), are associated with Purchase Intention. A cross-sectional survey was administered in Pune, Maharashtra, India. After data screening, 320 responses were retained and analyzed using multiple linear regression. All three predictors show positive, statistically significant relationships with purchase intention, and convenience has the largest standardized effect among the three factors studied. The result suggests a clear priority order: first, remove friction in the core journey (discovery, checkout, delivery, and returns), and then use personalization and SSR to increase relevance and strengthen trust. The study is limited by its single-city context and self-reported measures; future research can improve generalizability by sampling multiple regions and by combining survey measures with behavioral or transactional data.